Real Instant Road Condition Monitoring With Data Logging Algorithm

Most common problems that wear facing in our daily life is surface defects such as potholes, suck-in manholes, and defective street cuts are still commonly found on roads. Due to this current methods of road surface monitoring depends on reports of drivers. Conversely, these methods usually incur a long delay consequently it is difficult to obtain up-to-date information on road conditions. To conquer the drawbacks the current methods, we intend an innovative and automated method that uses mobile data collection kit mounted on vehicles. Here, we use a supervised machine learning technique to classify the collected data fragments into three different categories at a back-end server. The mobile data collection kit includes accelerometer, global positioning system (GPS) sensor and microcomputer. To increase the efficiency of data logging a local algorithm is embedded. Large amount of collected data are transmitted to a back-end server.